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Proceedings Paper

Novel binning method for improved accuracy and speed of volume image coregistration using normalized mutual information
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Paper Abstract

There is a growing consensus that mutual voxel information based measures hold great promise for fully automated multimodal image registration. We have found that image greyscale binning using a specific variation of contrast- limited histogram equalization (which we call histogram preservation) provides significant reduction of noise and spurious local maxima in the normalized mutual information function without causing significant displacement or smoothing of the global maximum. These effects are also relatively robust in the presence of image subsampling, so that accurate subpixel coregistration of typical medical volume images may be achieved in a few seconds by a very simple optima search algorithm based on a few thousand sampled voxels. In this paper, we illustrate these effects by presenting the results of random tests on patient data. Intramodal performance is evaluated by image self- registration using a variety of patient image volumes. Reregistration error is measured as the mean of the residual Euclidean displacement of the eight corner points of the image volumes after reregistration. The performance of histogram preservation prebinning is compared to linear prebinning, and the effect of image subsampling and number of bins on algorithm speed and accuracy is also assessed.

Paper Details

Date Published: 21 May 1999
PDF: 8 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348572
Show Author Affiliations
Jon J. Camp, Mayo Clinic and Foundation (United States)
Richard A. Robb, Mayo Clinic and Foundation (United States)

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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